Year QA Issue:
To Dos:
Presentation:
Notes from meeting with Peter, 4/13/18: Story board out the talk: what do I want the take home message to be?
*How should I address (in the presentation) that this is how the data is already presented in the annual review?
#Need to change to KNB url once it is generated
escapement_goals <- read.csv("~/R/Esc_goals_evaluated.csv", stringsAsFactors = FALSE)Changing some names:
escapement_goals$SASAP.Region[escapement_goals$SASAP.Region == "Alaska Peninsula and Aleutian Islands"] <- "AK Pen. and Aleutians"
escapement_goals$types[escapement_goals$types == "inRiver"] <- "In-River"
escapement_goals$types[escapement_goals$types == "agreement"] <- "Agreement"Region scale_fill_brewer("Colors in Spectral", palette = "Spectral")
Species scale_color_brewer(palette = "Paired")
Exceeded/met/unmet scale_fill_manual(values = "achievement_colors") indefinite work in progress
Show there are no goals in the Arctic by adding a region to the data set: this will be displayed in the table and plot that shows goal by region
Arctic <- data.frame("Arctic")
colnames(Arctic) <- "SASAP.Region"YearQA <- escapement_goals %>%
select(SASAP.Region, sampleYear) %>%
group_by(SASAP.Region) %>%
summarize(min(sampleYear),
max(sampleYear))
#Bristol Bay is the only region that includes 2017, so let's remove it (causing issues anyways)
escapement_goals_QAed <- escapement_goals %>%
filter(sampleYear < 2017) #This is done to ignore BB 2017 data, which is incomplete as of 5/2/18, but still included in cumulative escapement data
escapement_goals_QAedkable <- escapement_goals_QAed %>%
select(SASAP.Region, sampleYear) %>%
group_by(SASAP.Region) %>%
summarize(min(sampleYear),
max(sampleYear))
#Print a kable to show year ranges, and make note of this exclusion#Should make these tables include a list of goal types by region
kable(YearQA, caption = "Ranges of Years in Original Dataset")| SASAP.Region | min(sampleYear) | max(sampleYear) |
|---|---|---|
| AK Pen. and Aleutians | 1974 | 2016 |
| Bristol Bay | 1955 | 2017 |
| Chignik | 1922 | 2016 |
| Cook Inlet | 1960 | 2016 |
| Copper River | 2001 | 2015 |
| Kodiak | 1921 | 2016 |
| Kotzebue | 2001 | 2014 |
| Kuskokwim | 1969 | 2016 |
| Norton Sound | 1965 | 2015 |
| Prince William Sound | 1961 | 2015 |
| Southeast | 1953 | 2016 |
| Yukon | 1972 | 2016 |
Missing most recent 2 years: notice that not all regions have the same max year. Should I only evaluate thorough 2014? Seems rather silly given that would exclude all of Bristol Bay’s most recent large runs, as well as the past couple Kusko years of slight improvement.
kable(escapement_goals_QAedkable, caption = "Ranges of Years after removing incomplete 2017 data")| SASAP.Region | min(sampleYear) | max(sampleYear) |
|---|---|---|
| AK Pen. and Aleutians | 1974 | 2016 |
| Bristol Bay | 1955 | 2016 |
| Chignik | 1922 | 2016 |
| Cook Inlet | 1960 | 2016 |
| Copper River | 2001 | 2015 |
| Kodiak | 1921 | 2016 |
| Kotzebue | 2001 | 2014 |
| Kuskokwim | 1969 | 2016 |
| Norton Sound | 1965 | 2015 |
| Prince William Sound | 1961 | 2015 |
| Southeast | 1953 | 2016 |
| Yukon | 1972 | 2016 |
This may need some work
#Need to create a kable of this
#Do I really want to remove NA values? If they are included in the df, I can show locations of annual counts with no goals
#that way, EG_results would retains NAs, percents would omit them
EG_results <- escapement_goals_QAed %>%
filter(!is.na(MetLower) | !is.na(MetUpper)) %>%
arrange(SASAP.Region, LocationID)
sumna <- function(x) {
sum(x, na.rm = TRUE)
}
#Selecting a lot of columns here, but may eventually need to make a separate df: one with only a few pertinent columns for "percents", another with more data
percents <- EG_results %>%
#select(SASAP.Region, LocationID, Species, sampleYear, MetLower, MetUpper) %>%
select(SASAP.Region, LocationID, Species, sampleYear, annualCount, types, MetLower, MetUpper) %>%
group_by(sampleYear, SASAP.Region, types, LocationID, Species) %>%
summarise(countMetLower = sumna(MetLower),
countMetUpper = sumna(MetUpper),
notMetUpper = sumna(!MetUpper),
notMetLower = sumna(!MetLower),
percentMetUpper = (sumna(MetUpper)/(sumna(MetUpper) + sumna(!MetUpper))),
percentMetLower = (sumna(MetLower)/(sumna(MetLower) + sumna(!MetLower)))) %>%
arrange(SASAP.Region, LocationID)
percents$metUnmetExceeded = ifelse(percents$countMetUpper > 0, "Exceeded", ifelse(percents$countMetLower > 0, "Met", "Unmet"))
plottingdf <- bind_rows(percents, Arctic) %>%
arrange(SASAP.Region, LocationID)## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
Dataframes used throughout plotting
Frequently Used Policies of the Alaska Board of Fisheries
Sustainable Salmon Policy Established in 2000, followed by stamp of approcal from the Marine Stewardship Council Is this referring to the Policy for the management of sustainable fisheries?
Alaska’s Sustainable Salmon Fisheries Policy
Article II of the Alaska Administrative Code pertains to the governanace of salmon fisheries.
"Conservation of wild salmon stocks consistent with sustained yeild shall be accorded the highest priority." Which then also addresses allocation preferences.
"In the absence of a regulatory management plan, the burden of conservation shall be shared among all fisheries in close proportion to their respective harvest on the stock of concern... precise sharing of conservation among fisheries is dependent on the amount of stock-specific information available."
"Most wild Alaska salmon stocks are fully allocated to fisheries capable of harvesting available surpluses. Natural fluctuations in the abundance of stocks harvested in a fishery will not be the single factor that identifies a fishery as expanding or new. "
"Alaska's salmon fisheries are healthy and sustainable largely because of abundant pristine habitat and the application of sound, precautionary, conservation management practices, there is a need for a comprehensive policy for the regulation and management of sustainable salmon fisheries"
"In formulating fishery management plans designed to achieve maximum or optimum salmon production, the board and department must consider factors including environmental change, habitat loss or degradation, data uncertainty, limited funding for research and management programs, existing harvest patterns, and new fisheries or expanding fisheries"
"The goal of the policy under this section is to ensure conservation of salmon and salmon's required marine and aquatic habitats, protection of customary and traditional subsistence uses and other uses, and the sustained economic health of Alaska's fishing communities."
This section (a.1.c.1) the goes on to specify how conservation of wild salmon stocks and their habitats should be maintained at levels of resource productivity that assure sustained yield. It includes, “effects and interactions of introduced or enhanced salmon stocks on wild salmon stocks should be assessed; wild salmon stocks and fisheries on those stocks should be protected from adverse impacts from artificial propagation and enhancement efforts” as well as, “depleted salmon stocks should be allowed to recover or, where appropriate, should be actively restored; diversity should be maintained to the maximum extent possible, at the genetic, population, species, and ecosystem levels”
“Salmon fisheries shall be managed to allow escapements within ranges necessary to conserve and sustain potential salmon production and maintain normal ecosystem functioning as follows:
Regulation of human activities affecting salmon (harvest), public involvement in sustainable use and protection of salmon resources, and artificial propogation and habitat mangement in times of uncertainty are also addressed within a.1.c.
Stock assessements for the principles and criteria for susstainable salmon fisheries are determined.
The state is designated as the sole proprietor in Alaska salmon management: “Nothing in the policy under this section is intended to expand, reduce, or be inconsistent with, the statutory regulatory authority of the board, the department, or other state agencies with regulatory authority that impacts the fishery resources of the state.”
allocation: means the granting of specific harvest privileges, usually by regulation, among or between various user groups; “allocation” includes quotas, time periods, area restrictions, percentage sharing of stocks, and other management measures providing or limiting harvest opportunity
allocation criteria: means the factors set out in AS 16.05.251(e) considered by the board as appropriate to particular allocation decisions under 5 AAC 39.205, 5 AAC 75.017, and 5 AAC 77.007
BEG, biological escapement goal: the escapement that provides the greatest potential for maximum sustained yield; the primary management objective for the escapement unless an optimal escapement or inriver run goal has been adopted; BEG will be developed from the best available biological information, and should be scientifically defensible on the basis of available biological information; BEG will be determined by the department and will be expressed as a range based on factors such as salmon stock productivity and data uncertainty; the department will seek to maintain evenly distributed salmon escapements within the bounds of a BEG
conservation concern: concern arising from a chronic inability, despite the use of specific management measures, to maintain escapements for a stock above a sustained escapement threshold (SET); a conservation concern is more severe than a management concern
depeleted salmon stock: a salmon stock for which there is a conservation concern
diversity: in a biological context, means the range of variation exhibited within any level of organization, such as among genotypes within a salmon population, among populations within a salmon stock, among salmon stocks within a species, among salmon species within a community, or among communities within an ecosystem
Enhanced salmon stock: a stock of salmon that is undergoing specific manipulation, such as hatchery augmentation or lake fertilization, to enhance its productivity above the level that would naturally occur; includes an introduced stock, where no wild salmon stock had occurred before, or a wild salmon stock undergoing manipulation, but does not include a salmon stock undergoing rehabilitation, which is intended to restore a salmon stock’s productivity to a higher natural level
Escapement: the annual estimated size of the spawning salmon stock; quality of the escapement may be determined not only by numbers of spawners, but also by factors such as sex ratio, age composition, temporal entry into the system, and spatial distribution within the salmon spawning habitat
expanding fishery: a salmon fishery in which effective harvesting effort has recently increased significantly beyond historical levels and where the increase has not resulted from natural fluctuations in salmon abundance
expected yields: levels at or near the lower range of recent historic harvests if they are deemed sustainable
genetic: those characteristics (genotypic) of an individual or group of salmon that are expressed genetically, such as allele frequencies or other genetic markers
habitat concern: the degradation of salmon habitat that results in, or can be anticipated to result in, impacts leading to yield, management, or conservation concerns
Harvestable surplus: the number of salmon from a stock’s annual run that is surplus to escapement needs and can reasonably be made available for harvest
healthy salmon stock: a stock of salmon that has annual runs typically of a size to meet escapement goals and a potential harvestable surplus to support optimum or maximum sustained yield
incidental harvest: the harvest of fish, or other species, that is captured in addition to the target species of a fishery
incidental mortality: the mortality imposed on a salmon stock outside of directed fishing, and mortality caused by incidental harvests, interaction with fishing gear, habitat degradation, and other human-related activities
Inriver run goal: a specific management objective for salmon stocks that are subject to harvest upstream of the point where escapement is estimated; the inriver run goal will be set in regulation by the board and is comprised of the SEG, BEG, or OEG, plus specific allocations to inriver fisheries
management concern: a concern arising from a chronic inability, despite use of specific management measures, to maintain escapements for a salmon stock within the bounds of the SEG, BEG, OEG, or other specified management objectives for the fishery; a management concern is not as severe as a conservation concern
MSY, maximum sustained yield: the greatest average annual yield from a salmon stock; in practice, MSY is achieved when a level of escapement is maintained within a specific range on an annual basis, regardless of annual run strength; the achievement of MSY requires a high degree of management precision and scientific information regarding the relationship between salmon escapement and subsequent return; the concept of MSY should be interpreted in a broad ecosystem context to take into account species interactions, environmental changes, an array of ecosystem goods and services, and scientific uncertainty
OEG, optimal escapement goal: a specific management objective for salmon escapement that considers biological and allocative factors and may differ from the SEG or BEG; an OEG will be sustainable and may be expressed as a range with the lower bound above the level of SET, and will be adopted as a regulation by the board; the department will seek to maintain evenly distributed escapements within the bounds of the OEG
OSY, optimum sustained yield: an average annual yield from a salmon stock considered to be optimal in achieving a specific management objective other than maximum yield, such as achievement of a consistent level of sustained yield, protection of a less abundant or less productive salmon stock or species, enhancement of catch per unit effort in sport fishery, facilitation of a nonconsumptive use, facilitation of a subsistence use, or achievement of a specific allocation
overfishing:a level of fishing on a salmon stock that results in a conservation or management concern
phenotypic characteristics: those characteristics of an individual or group of salmon that are expressed physically, such as body size and length at age
rehabilitation: efforts applied to a salmon stock to restore it to an otherwise natural level of productivity; “rehabilitation” does not include an enhancement, which is intended to augment production above otherwise natural levels
return: the total number of salmon in a stock from a single brood (spawning) year surviving to adulthood; because the ages of adult salmon (except pink salmon) returning to spawn varies, the total return from a brood year will occur over several calendar years; the total return generally includes those mature salmon from a single brood year that are harvested in fisheries plus those that compose the salmon stock’s spawning escapement; “return” does not include a run, which is the number of mature salmon in a stock during a single calendar year
run: the total number of salmon in a stock surviving to adulthood and returning to the vicinity of the natal stream in any calendar year, composed of both the harvest of adult salmon plus the escapement; the annual run in any calendar year, except for pink salmon, is composed of several age classes of mature fish from the stock, derived from the spawning of a number of previous brood years
salmon population: a locally interbreeding group of salmon that is distinguished by a distinct combination of genetic, phenotypic, life history, and habitat characteristics, comprised of an entire stock or a component portion of a stock; the smallest uniquely identifiable spawning aggregation of genetically similar salmon used for monitoring purposes
salmon stock: a locally interbreeding group of salmon that is distinguished by a distinct combination of genetic, phenotypic, life history, and habitat characteristics or an aggregation of two or more interbreeding groups which occur within the same geographic area and is managed as a unit
**stock of concern: a stock of salmon for which there is a yield, management, or conservation concern::
SEG, sustainable escapement goal: a level of escapement, indicated by an index or an escapement estimate, that is known to provide for sustained yield over a 5 to 10 year period, used in situations where a BEG cannot be estimated or managed for; the SEG is the primary management objective for the escapement, unless an optimal escapement or inriver run goal has been adopted by the board; the SEG will be developed from the best available biological information; and should be scientifically defensible on the basis of that information; the SEG will be determined by the department and will take into account data uncertainty and be stated as either a “SEG range” or “lower bound SEG”; the department will seek to maintain escapements within the bounds of the SEG range or above the level of a lower bound SEG
sustainable salmon fishery: a salmon fishery that persists and obtains yields on a continuing basis; characterized by fishing activities and habitat alteration, if any, that do not cause or lead to undesirable changes in biological productivity, biological diversity, or ecosystem structure and function, from one human generation to the next
susatined yield: an average annual yield that results from a level of salmon escapement that can be maintained on a continuing basis; a wide range of average annual yield levels is sustainable; a wide range of annual escapement levels can produce sustained yields
SET, sustainted escapement threshold: a threshold level of escapement, below which the ability of the salmon stock to sustain itself is jeopardized; in practice, SET can be estimated based on lower ranges of historical escapement levels, for which the salmon stock has consistently demonstrated the ability to sustain itself; the SET is lower than the lower bound of the BEG and lower than the lower bound of the SEG; the SET is established by the department in consultation with the board, as needed, for salmon stocks of management or conservation concern
target species, or target salmon stock: the main, or several major, salmon species of interest toward which a fishery directs its harvest
yield: the number or weight of salmon harvested in a particular year or season from a stock
yield concern: a concern arising from a chronic inability, despite the use of specific management measures, to maintain expected yields, or harvestable surpluses, above a stock’s escapement needs; a yield concern is less severe than a management concern, which is less severe than a conservation concern
Wild salmon stock: a stock of salmon that originates in a specific location under natural conditions; may include an enhanced or rehabilitated stock if its productivity is augmented by supplemental means, such as lake fertilization or rehabilitative stocking; “wild salmon stock” does not include an introduced stock, except that some introduced salmon stocks may come to be considered “wild” if the stock is self-sustaining for a long period of time
action point: a threshold value for some quantitative indicator of stock run strength at which an explicit management action will be taken to achieve an optimal escapement goal
"The DFG and the BoF are charged with the duty to conserve and develop Alaska's salmon fisheries on the sustained yield principle. Therefore, the establishment of salmon escapement goals is the responsibility of both the board and the department working collaboratively. The purpose of this policy is to establish the concepts, criteria, and procedures for establishing and modifying salmon escapement goals and to establish a process that facilitates public review of allocative issues associated with escapement goals."
“The board recognizes the department’s responsibility to
"In recognition of its joint responsibilities, and in consultation with the department, the board will
“Unless the context requires otherwise, the terms used in this section have the same meaning given those terms in 5 AAC 39.222(f).”
The AAC also dictates fishing gear specifications and operations, district boundaries; and in the past has addressed specialized management plans in the past, such as the AYK Region Chum Salmon Rebuilding Management Plan.
Goal Types
ggplot(data = plottingdf, aes(x = SASAP.Region, fill=factor(types), y=(..count..))) +
geom_bar() +
labs(x = "",
y = "Number of Goals",
title = "Escapement Goals by Region") +
ggtitle("Escapement Goals by Region") +
theme_hc() +
theme(aspect.ratio = 0.5,
plot.background = element_rect(fill="gray96"),
legend.text = element_text(size=8),
legend.position = "right",
legend.background = element_rect(fill="gray96"),
panel.grid.major.y = element_line(color="gray93"),
axis.ticks.x = element_line(color = "gray96")) +
guides(fill=guide_legend("Goal Type"), legend.title=element_text(size=10)) +
theme(axis.text.x = element_text(angle = 50, hjust = 0.85)) +
scale_fill_brewer("Colors in Spectral", palette="Spectral", breaks = c("agreement", "BEG", "inRiver", "MT", "OEG", "SEG")) f <- function(x){
format(round(x, 0), nsmall=0, scientific=FALSE)
}
ggplot(data = plottingdf, aes(x = sampleYear, fill=factor(types), y=(..count..))) +
geom_bar(width=1) +
labs(x = "Year",
y = "Number of Goals",
title = "Escapement Goals by Region") +
scale_y_continuous(labels = f) +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.text = element_text(size=8),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
axis.text.x = element_text(angle = 50, hjust = 0.95),
strip.background = element_rect(fill = "gray98"),
panel.grid.major.y = element_line(color="gray93")) +
guides(fill=guide_legend("Goal Type")) +
scale_fill_brewer("Colors in Spectral", palette="Spectral") +
facet_wrap(~SASAP.Region, scales="free_y", ncol = 4)## Warning: Removed 1 rows containing non-finite values (stat_count).
#I would like to try this as a geom_area, but I need to figure out the percents df beforehand
#WHY IS KOTZEBUE STILL GIVING ME ISSUES?!inRiver <- plottingdf %>%
group_by(types) %>%
filter(types == "inRiver")
Agreement <- escapement_goals %>%
group_by(types) %>%
filter(types == "Agreement")
MT <- plottingdf %>%
group_by(types) %>%
filter(types == "MT")All inRiver goals are Southeast Chilkat Chinook
All Agreement goals are Yukon “Mainstem Yukon River (Canada)” and “Fishing Branch River”
All MT goals are Southeast Taku River
ggplot(data = EG_results, aes(x = sampleYear, fill=factor(types), y=(..count..))) +
geom_bar() +
labs(x = "Year",
y = "Number of Goals",
title = "Escapement Goals by Species") +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
theme_hc() +
theme(aspect.ratio = 0.2,
plot.background = element_rect(fill="gray98"),
legend.text = element_text(size=8),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
axis.text.x = element_text(angle = 50, hjust = 0.95),
strip.background = element_rect(fill = "gray98"),
panel.grid.major.y = element_line(color="gray93")) +
guides(fill=guide_legend("Goal Type"), legend.title=element_text(size=10)) +
scale_fill_brewer("Colors in Spectral", palette="Spectral") +
facet_wrap(~Species, scales="free_y", ncol=1)Years that all = 100% are suspect, need some QA
ggplot(data = percents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs( x = "Year",
y = "Proportion of Regions",
title = "Annual Goal Achievement, Statewide") +
scale_y_continuous(labels=percent) +
scale_x_continuous(breaks = pretty_breaks(n = 5)) +
guides(fill=guide_legend(title="")) +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
panel.grid.major.y = element_line(color="gray93")) +
scale_fill_manual(values=achievement_colors) #scale_fill_brewer(palette = "YlGnBu", direction = -1)
#We're getting there with the colors
#Might need to change the aspect ratio, make everything a bit more tight/closer together#It's easier to compare by region and year with less columns, but we still need to stretch out the y axis
ggplot(data = percents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Proportio of Years",
title = "Annual Goal Achievement") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~SASAP.Region, ncol = 2) +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.text = element_text(size=8),
legend.position = "bottom",
legend.background = element_rect(fill="gray98"),
strip.background = element_rect(fill = "gray98"),
panel.grid.major.y = element_line(color="gray93")) +
scale_fill_manual(values=achievement_colors)ggplot(data = AKAlpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Peninsula and Aleutians") +
scale_y_continuous(labels=percent) +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol=1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = BBpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Bristol Bay") +
scale_y_continuous(labels=percent) +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol=1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)BB_esc <- EG_results %>%
filter(SASAP.Region == "Bristol Bay") %>%
group_by(sampleYear, Species) %>%
summarize(sum(annualCount))
colnames(BB_esc) <- c("Year", "Species", "SumAnnualCount")#Too add in a line of the goals, we would need to visualize by LocationID, not by entire region
ggplot(data = BB_esc, aes(x = Year, y = SumAnnualCount, color = Species)) +
geom_line(size = 1) +
expand_limits(y=c(0,1000000)) +
scale_y_continuous(labels=comma) +
labs(x = "Year",
y = "Annual Count",
title = "Bristol Bay Escapement",
subtitle = "all systems within the region") +
guides(fill=guide_legend(title="Species")) +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
panel.grid.major.y = element_line(color="gray93"),
panel.grid.major.x = element_line(color="gray93"),
axis.line = element_line(color="gray80")) +
scale_color_manual(values = species_color)BBesc_summary1 <- BB_esc %>%
group_by(Species) %>%
summarize(min(Year), max(Year))
kable(BBesc_summary1, caption = "Bristol Bay Data Coverage")| Species | min(Year) | max(Year) |
|---|---|---|
| chinook | 2007 | 2016 |
| chum | 2007 | 2016 |
| coho | 2013 | 2014 |
| pink | 2013 | 2014 |
| sockeye | 1984 | 2016 |
BBesc_summary2 <- BBpercents %>%
group_by(Species) %>%
summarize(min(sampleYear), max(sampleYear))
kable(BBesc_summary2, caption = "Bristol Bay Data Coverage")| Species | min(sampleYear) | max(sampleYear) |
|---|---|---|
| chinook | 2007 | 2016 |
| chum | 2007 | 2016 |
| coho | 2013 | 2014 |
| pink | 2013 | 2014 |
| sockeye | 1984 | 2016 |
BBesc_summary3 <- escapement_goals %>%
filter(SASAP.Region == "Bristol Bay") %>%
group_by(Species) %>%
summarize(min(sampleYear), max(sampleYear))
kable(BBesc_summary3, caption = "Bristol Bay Data Coverage")| Species | min(sampleYear) | max(sampleYear) |
|---|---|---|
| chinook | 2001 | 2016 |
| chum | 2001 | 2016 |
| coho | 2012 | 2014 |
| pink | 2012 | 2014 |
| sockeye | 1955 | 2017 |
BBesc_summary4 <- escapement_goals_QAed %>%
filter(SASAP.Region == "Bristol Bay") %>%
group_by(Species) %>%
summarize(min(sampleYear), max(sampleYear))
kable(BBesc_summary4, caption = "Bristol Bay Data Coverage")| Species | min(sampleYear) | max(sampleYear) |
|---|---|---|
| chinook | 2001 | 2016 |
| chum | 2001 | 2016 |
| coho | 2012 | 2014 |
| pink | 2012 | 2014 |
| sockeye | 1955 | 2016 |
ggplot(data = CHIGpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Chignik") +
scale_y_continuous(labels=percent) +
scale_x_continuous(breaks = pretty_breaks(n = 10)) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol=1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = CIpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Cook Inlet") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = CRpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Copper River") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = KODpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Kodiak") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = KOTZpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Kotzebue") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = KUSKOpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Kuskokwim") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)#Here I need to look at at different df to get the above desired output.
Kusko_esc <- EG_results %>%
filter(SASAP.Region == "Kuskokwim") %>%
group_by(sampleYear, Species) %>%
summarize(sum(annualCount))
colnames(Kusko_esc) <- c("Year", "Species", "SumAnnualCount")#Too add in a line of the goals, we would need to visualize by LocationID, not by entire region
ggplot(data = Kusko_esc, aes(x = Year, y = SumAnnualCount, color = Species)) +
geom_line(size = 1) +
expand_limits(y=c(0,1000000)) +
scale_y_continuous(labels=comma) +
labs(x = "Year",
y = "Annual Count",
title = "Kuskokwim Escapement",
subtitle = "all systems within the region") +
guides(fill=guide_legend(title="Species")) +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
panel.grid.major.y = element_line(color="gray93"),
panel.grid.major.x = element_line(color="gray93"),
axis.line = element_line(color="gray80")) +
scale_color_manual(values = species_color) #geom_line(data = EG_results, aes(x = sampleYear, y = upper_goal, group = year_implemented, color = factor(goal_type)), linetype = "dashed") +
#geom_line(aes(x = Year, y = lower_goal, group = year_implemented, color = factor(goal_type)), linetype = "dashed")
#show_col(species_color)ggplot(data = NSpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Norton Sound") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = PWSpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Prince William Sound",
subtitle = "excluding Copper River") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = SEpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Southeast") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)ggplot(data = YUKpercents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement: Yukon") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol = 1, scales="free_y") +
theme_minimal() +
scale_fill_manual(values = achievement_colors)This plot still needs some work
Need to explore gaps in chinook, years with 100% same value
ggplot(data = percents, aes(x = sampleYear, fill=factor(metUnmetExceeded), y=(..count..))) +
geom_bar(position='fill', width = 1) +
labs(x = "Year",
y = "Percentage Achieved",
title = "Annual Goal Achievement by Species, Statewide") +
scale_y_continuous(labels=percent) +
guides(fill=guide_legend(title="")) +
facet_wrap(~Species, ncol=1, scales="free_y") +
theme_hc() +
theme(plot.background = element_rect(fill="gray98"),
legend.text = element_text(size=8),
legend.position = "right",
legend.background = element_rect(fill="gray98"),
strip.background = element_rect(fill = "gray98"),
panel.grid.major.y = element_line(color="gray93")) +
scale_fill_manual(values = achievement_colors)This doesn’t really make sense, because it is by region and not locationID: meaningless because goals are established and evaluated by location, so maybe this should be done by a few select (case studies) locations
#species_percents <- percents %>%
# group_by(SASAP.Region, Species, sampleYear) %>%
# summarize(sum(metUnmetExceeded))
ggplot(data = percents, aes(x = sampleYear, y = percentMetLower, color=Species)) +
geom_line() +
labs(title = "Annual Lower Goal Achievement",
x = "Year",
y = "Proportion of Years Met") +
scale_y_continuous(labels=percent) +
facet_wrap(~SASAP.Region) +
theme_minimal() +
scale_color_brewer(palette="Paired")Conclusion of general overview: what do I want to say here?
Specific examples: “other species are doing well” (show that) “but let’s take a look at chinook” (show examples);
Show across the board, big scale overview opening: use this as a way to show what’s happening on the ground;
Use that as a segue to show what’s going on on the grounds
Show mean, min, and max of brood tables across regions?
Should I select a timeframe that works for all tables/datasets?
Focus on Western AK sockeye: that is where the data abundance is
Focus on Chinook: conservation case studies, comparison among regions
“To ensure sustainability, ADF&G establishes escapement goals—a desired range in the number of fish escaping capture in the commercial fishery and returning to the spawning grounds of each river—and permits fishing in the district at the mouth of each river only when escapement is at or above the historic arrival pace that supports meeting those escapement objectives. While larger escapements in most rivers are associated with higher expected future returns, historical observations at higher levels over the last 120 years are infrequent and outcomes ambiguous. This variability is reflected in the precautionary nature of the status quo escapement goals, labeled Current Sustainable Escapement Goals (SEG). Escapement is carefully tracked within the season at enumeration sites (counting towers or fixed sonar sites) on each river, and the number of landed fish is estimated on a daily basis from the observed weight of the catch, the Bristol Bay salmon fishery is perhaps the most intensively managed fishery in the world. It is Marine Stewardship Council certified.”
“As part of an every-three-year review process using the latest stock-recruit data, Fair et al. (2012) suggested that raising the escapement goals—considerably for Egegik and Ugashik—to the Biological Escapement Goals (BEGs) in Table 2, would increase yield from the fishery. Harvest achieved by targeting these BEGs was expected to more closely reflect maximum sustainable yield (MSY). Alaska’s Policy for the Management of Sustainable Salmon Fisheries specifies that, to the extent possible, salmon fisheries are to be managed for MSY, which depends on sufficient historical stock-recruit data to define MSY escapement. (5 AAC 39.222).”
[Table 2](/Users/MadiMac/SASAP Work/’Mo fish ’mo money plots/Table 2.png)
MSY: greater fish availability = more fish to sell -> more fishing income and jobs
Bue et al. (2008) showed that economic profitability was influenced by limits on processing and harvesting capacity, and industry intuitively understood that bigger runs do not translate directly into greater economic performance.
Current bay-wide processing capacity is around 1.8 million fish per day.
One day of high catch has two consequences for processors:
The bioeconomic picture of Bristol Bay is a complex one, where the value of the catch is limited by available fish in low-run years, but also by processing capacity in high run years. The value of fish on peak run days is eroded through processing into lower value products; in the highest run years, this value is entirely dissipated because capacity constraints allow it to escape. As a result, increases in average run size that also increase the variance of potential catch may not result in more fish being landed and processed, leading some authors to suggest a constant harvest management strategy (Steiner et al. 2011). Further, increasing catch variability is not distributionally neutral because, while individual-river variability can be mitigated by switching to other rivers during the season, harvesters differ in their ability to do this.
Understanding how three proposed escapement goal policies attain economic and community objectives for the fishery therefore requires:
Harvests, and thus stock size and fishery benefits, will be based on processors’ long-run plant scale choices, which will dictate the size of the work force chosen operate the lines they can keep busy most days of the season, and in turn constrain the product mix, which is determined by the shape and timing of the run as much as its size. This paper describes an integrated bioeconomic management strategy evaluation (MSE) that quantifies the tradeoff between the average yield and the variance in yield, which provided regulators with guidance on designing harvest policies for environments where production variability is a major factor in shaping outcomes for industry and fishing communities.
MSE Methods: models are used as the basis for 100-year forward simulations of stock and processing industry, project mean and variance of revenues to key participants under alternative escapement goal policies
Forward Simulation: lnk processor revenue and product mix to a division of revenue betwen processors and harvestors
So for my understanding: Increasing current SEGs to higher levels will increase escapement needed before openings are allowed, therefore decreasing fishing opportunitiy during maximum years (but not making any change during low volume years?); This would in-turn decrease frequency of limits imposed by processors, allowing more consistent products manufactored, which would allow for a more consistent market, and therefore give fishermen a more consistent exvessel price. Correct? So what about other fisheries that don’t have these variance issues?
Still need to go through results and apply appropriate tables and figures